Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	17,511
2	New Jersey	15,596
3	Massachusetts	11,286
4	Rhode Island	10,642
5	Connecticut	9,411
6	District of Columbia	8,887
7	Louisiana	6,797
8	Delaware	6,620
9	Illinois	6,128
10	Maryland	5,407
11	Michigan	4,717
12	Pennsylvania	4,689
13	Nebraska	4,305
14	South Dakota	3,975
15	Iowa	3,790
16	Indiana	3,658
17	Colorado	3,402
18	Mississippi	3,192
19	Georgia	3,039
20	Virginia	2,821
21	Kansas	2,418
22	Washington	2,351
23	New Mexico	2,319
24	New Hampshire	2,258
25	Tennessee	2,205
26	Ohio	2,060
27	Alabama	2,016
28	Minnesota	1,998
29	Nevada	1,996
30	North Dakota	1,956
31	Utah	1,949
32	Florida	1,889
33	Wisconsin	1,755
34	California	1,722
35	Missouri	1,614
36	Arizona	1,527
37	South Carolina	1,486
38	Vermont	1,485
39	Kentucky	1,451
40	North Carolina	1,409
41	Texas	1,380
42	Arkansas	1,329
43	Idaho	1,248
44	Oklahoma	1,159
45	Wyoming	1,143
46	Maine	1,068
47	Oregon	765
48	West Virginia	759
49	Puerto Rico	688
50	Alaska	516
51	Hawaii	438
52	Montana	428

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	District of Columbia	291
2	Rhode Island	234
3	South Dakota	230
4	Massachusetts	196
5	Nebraska	196
6	New Jersey	183
7	Illinois	180
8	Maryland	177
9	Delaware	173
10	Connecticut	165
11	New York	132
12	Minnesota	112
13	Pennsylvania	101
14	Virginia	98
15	Iowa	95
16	Kansas	92
17	Mississippi	91
18	Indiana	83
19	Colorado	77
20	Louisiana	67
21	New Mexico	58
22	Alabama	57
23	Wisconsin	57
24	New Hampshire	55
25	Ohio	55
26	Georgia	54
27	Utah	54
28	Arizona	53
29	North Dakota	52
30	Michigan	49
31	California	46
32	Tennessee	43
33	North Carolina	42
34	Texas	38
35	Arkansas	35
36	South Carolina	33
37	Nevada	28
38	Florida	27
39	Maine	26
40	Missouri	26
41	Washington	25
42	Kentucky	23
43	Oklahoma	21
44	Oregon	18
45	Puerto Rico	17
46	Wyoming	15
47	West Virginia	12
48	Idaho	10
49	Vermont	5
50	Alaska	2
51	Hawaii	0
52	Montana	0

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New York	1,377
2	New Jersey	1,041
3	Connecticut	832
4	Massachusetts	722
5	Louisiana	476
6	District of Columbia	457
7	Michigan	455
8	Rhode Island	398
9	Pennsylvania	298
10	Maryland	271
11	Illinois	270
12	Delaware	230
13	Indiana	223
14	Colorado	168
15	Mississippi	144
16	Georgia	130
17	Washington	123
18	Ohio	114
19	Minnesota	102
20	Nevada	99
21	Virginia	98
22	New Hampshire	97
23	New Mexico	95
24	Vermont	86
25	Iowa	83
26	Missouri	81
27	Alabama	80
28	Florida	80
29	Arizona	73
30	California	70
31	Kentucky	69
32	Oklahoma	68
33	Wisconsin	68
34	South Carolina	64
35	Kansas	59
36	North Carolina	53
37	Nebraska	50
38	Maine	47
39	North Dakota	45
40	South Dakota	38
41	Texas	38
42	Idaho	37
43	Tennessee	35
44	Puerto Rico	34
45	Arkansas	30
46	Oregon	30
47	West Virginia	30
48	Utah	20
49	Montana	14
50	Hawaii	12
51	Wyoming	12
52	Alaska	10

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Massachusetts	20
2	District of Columbia	17
3	New Jersey	17
4	Connecticut	15
5	New York	10
6	Rhode Island	10
7	Delaware	7
8	Illinois	7
9	Maryland	7
10	Michigan	6
11	Louisiana	5
12	Pennsylvania	5
13	Indiana	4
14	Minnesota	4
15	New Hampshire	4
16	New Mexico	4
17	Arizona	3
18	Iowa	3
19	Mississippi	3
20	Missouri	2
21	Virginia	2
22	Alabama	1
23	California	1
24	Colorado	1
25	Florida	1
26	Georgia	1
27	Nebraska	1
28	Nevada	1
29	North Carolina	1
30	North Dakota	1
31	Ohio	1
32	Oklahoma	1
33	South Dakota	1
34	Texas	1
35	Washington	1
36	Wisconsin	1
37	Alaska	0
38	Arkansas	0
39	Hawaii	0
40	Idaho	0
41	Kansas	0
42	Kentucky	0
43	Maine	0
44	Montana	0
45	Oregon	0
46	Puerto Rico	0
47	South Carolina	0
48	Tennessee	0
49	Utah	0
50	Vermont	0
51	West Virginia	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	120,259	1	99
Lincoln	Arkansas	71,944	2	99
Dakota	Nebraska	70,259	3	99
Nobles	Minnesota	58,024	4	99
Cass	Indiana	40,383	5	99
Davidson	Tennessee	5,261	232	92
Richland	South Carolina	2,646	566	81
Pierce	Washington	1,872	790	74
Orange	California	1,104	1203	61
York	South Carolina	847	1472	53

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Randolph	Georgia	3,098	1	99
Early	Georgia	2,552	2	99
Terrell	Georgia	2,462	3	99
Essex	New Jersey	1,781	4	99
Nassau	New York	1,778	5	99
Richland	South Carolina	130	468	85
Pierce	Washington	66	771	75
Davidson	Tennessee	50	925	70
Orange	California	24	1256	60
York	South Carolina	18	1355	56

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.